Fast and Robust Association Tests for Untyped SNPs in Case-Control Studies

Abstract

Genome-wide association studies (GWASs) aim to genotype enough single nucleotide polymorphisms (SNPs) to effectively capture common genetic variants across the genome. Even though the number of SNPs genotyped in such studies can exceed a million, there is still interest in testing association with SNPs that were not genotyped in the study sample. Analyses of such untyped SNPs can assist in signal localization, permit cross-platform integration of samples from separate studies, and can improve power – especially for rarer SNPs. External information on a larger collection of SNPs from an appropriate reference panel, comprising both SNPs typed in the sample and the untyped SNPs we wish to test for association, is necessary for an untyped variant analysis to proceed. Linkage disequilibrium patterns observed in the reference panel are then used to infer the likely genotype at the untyped SNPs in the study sample. We propose here a novel statistical approach for testing untyped SNPs in case-control GWAS, based on an efficient score function derived from a prospective likelihood, that automatically accounts for the variability in the process of estimating the untyped variant. Computationally efficient methods of phasing can be used without affecting the validity of the test, and simple measures of haplotype sharing can be used to infer genotypes at the untyped SNPs, making our approach computationally much faster than existing approaches for untyped analysis. At the same time, we show, using simulated data, that our approach often has performance nearly equivalent to hidden Markov methods of untyped analysis. The software package ‘untyped’ is available to implement our approach.

An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

“…In GWAS, the case for including multiple loci is arguably even stronger, as the confounding effects of background loci may be genome-wide (due to linkage disequilibrium) rather than just local (due to linkage) 18 . Thus, while conditioning on known causative factors in GWAS has typically been done on a local scale, to help identify multiple alleles and clarify complex associations 12 , 19 , 20 , we believe that it should be done on a genome-wide basis. As illustrated in Fig.…”

Imputation Without Doing Imputation: A New Method for the Detection of Non‐Genotyped Causal Variants

“…Our method has some similarities with previously proposed multimarker tests that either directly test sets of adjacent (or nearby) SNPs [Chapman et al., 2003 ; Clayton et al., 2004 ; Humphreys and Iles, 2005 ; Kim et al., 2010 ; Slavin et al., 2011 ; Wason and Dudbridge, 2010 ] or else that construct tests at untyped SNPs on the basis of the multimarker genotypes observed [Allen et al., 2010 ; de Bakker et al., 2005 ; Lin et al., 2008 ; Nicolae, 2006 ; Pe'er et al., 2006 ]. A full comparison of our approach with these previously proposed methods is beyond the scope of the current manuscript, but would be an interesting topic for further investigation.…”

“…Approaches that rely on observed multimarker genotypes to construct tests at untyped SNPs have a close relationship to imputation-based approaches, relying on a reference panel (such as HapMap or 1000 Genomes) to infer information at untyped SNPs on the basis of their correlation pattern (in the reference panel) with genotyped SNPs. These approaches can be computationally intensive although some faster implementations do exist [Allen et al, 2010]. Approaches that directly test sets of adjacent (or nearby) SNPs, have been shown to improve power over single-SNP testing in simulation studies [Kim et al, 2010;Wason and Dudbridge, 2010], although it is less clear whether this power improvement is always achieved in practice: Slavin et al [2011] had some success when applying this approach to GWAS data for coronary artery disease and hypertension, while Wason and Dudbridge [2010] had less success applying it to schizophrenia.…”